Wi-Fi has moved from a nascent technology to one that is widely accepted and become so commonplace that we wonder how we ever functioned without it. It started from autonomous access points and was followed up by controller-based architecture (with a centralized controller and thin access points). And, as we learned from the challenges in deploying Wi-Fi and the ability of the environment to impact user experience, companies have constantly tried to innovate. Some focused on building dynamic channel or power planning, some built controller-less networks, and others tried to make it work in single channel. Of course, the discussion around the evolution of Wi-Fi architectures is not complete without talking about the adaptive beamforming antenna technologies.

This week we take a look at some recent developments in the realms of personalized shopping and artificial intelligence (AI) in ecommerce. Retailers, digital marketplaces, social networks and department stores are all trying to figure out the best way to deliver personalized, data-driven customer experience. Many companies see artificial intelligence as the answer to improve the shopping interface and user experience, while other marketers turn to personalized targeting methods and omni-channel strategies. In exploring these technological advances, it must be understood that not all innovations are profitable. These ideas have promises and risks, and their efficacy will ultimately be tested and decided by the customer. Last week, eBay acquired Expertmaker, a Swedish company that focused on artificial intelligence, machine learning and big data analytics.

In its quest to maintain a United States military advantage, the Pentagon is aggressively turning to Silicon Valley's hottest technology -- artificial intelligence. On Wednesday, Secretary of Defense Ashton B. Carter made his fourth trip to the tech industry's heartland since being named to his post last year. Before that, it had been 20 years since a defense secretary had visited the area, he noted in a speech at a Defense Department research facility near Google's headquarters. The Pentagon's intense interest in A.I. -- and by connection the Silicon Valley companies specializing in that technology -- has grown out of the "Third Offset" strategy articulated by Mr. Carter last fall. Concerned about the re-emergence of China and Russia as military competitors, he stated that computer-based, high-tech weapons would give the American military an edge in the future.

In the high tech world, AI has recently gained lots of traction as it seems to be a crucial way to deliver business value out of the big data as well described in Is Big Data Still a Thing? But recently, a day after Microsoft introduced an innocent AI chat robot to Twitter it had to be deleted after it transformed itself into a very evil one. It shows that AI needs to be well trained otherwise it can easily go out of track. When doing business, this is the last thing you want to happen: it's better to know where you're going. Our customers or prospects often have questions such as "what's the difference between Machine Learning and Deep Learning?",

Hyperparameter optimization is a common problem in machine learning. Machine learning algorithms, from logistic regression to neural nets, depend on well tuned hyperparameters to reach maximum effectiveness. Different hyperparameter optimization strategies have varied performance and cost (in time, money, and compute cycles.) So how do you choose? Evaluating optimization strategies is non-intuitive.

Let's suppose you've agreed to a rather odd travel program, where you're going to be suddenly transported to a randomly selected country, and your job is to figure out where you end up. So, here you are in some new country, and all countries are equally likely. You make a list of places and probabilities that you're in those places (all equally likely at about 1/200 for 200 countries). You look around and appear to be in a restaurant. Some countries have more restaurants (per capita/per land area) than others, so you decrease the odds that you're in Algeria or Sudan and increase the odds that you're in Singapore or other high-restaurant-density places. That is, you just multiply the probability that you were in a country with the probability of finding oneself in a restaurant in that country, given that one were already in the country, to obtain the new probability. After a few moments, the waitress brings you sushi, so you decrease the odds for Tajikistan and Paraguay and correspondingly increase the odds on Japan, Taiwan, and such places where sushi restaurants are relatively common. You pick up the chopsticks and try the sushi, discovering that it's excellent. Japan is now by far the most likely place, and though it's still possible that you're in the United States, it's not nearly as likely (sadly for the US). Those "probabilities" are getting really hard to read with all those zeros in front. All that matters is the relatively likelihood, so perhaps you scale that last column by the sum of the whole column. Now it's a probability again, and it looks something like this: Now that you're pretty sure it's Japan, you make a new list of places inside Japan to see if you can continue to narrow it down. You write out Fukuoka, Osaka, Nagoya, Hamamatsu, Tokyo, Sendai, Sapporo, etc., all equally likely (and maybe keep Taiwan too, just in case). Now the waitress brings unagi. You can get unagi anywhere, but it's much more common in Hamamatsu, so you increase the odds on Hamamatsu and slightly decrease the odds everywhere else. By continuing in this manner, you may eventually be able to find that you're eating at a delicious restaurant in Hamamatsu Station -- a rather lucky random draw.

This final state, which is stored in state is then used as the initial state in this line feed {self.input_data: The state variable has the real state information. When you construct the feed dictionary with self.initial_state:state, it basically means use state (the actual state value) as self.initial_state

Your next trip could be planned by... artificial intelligence? A new startup called Lola aims to revolutionize the concept of travel agents by making trip booking as easy as texting a friend. The company, which was started by Kayak founder Paul English, officially launches for iPhones Thursday, with a small team of travel bookers who will rely on super-powered artificial intelligence algorithms that will help them manage as many as hundreds of clients at once. Here's how it works: After setting up an account--and punching in credit card details--Lola users can simply message a representative through the app, whether they need a simple round trip to Chicago or want to plan a weeklong hiking expedition through Indonesia. While a traditional booking site would make you type in dates, preferences, and countless other parameters, Lola promises to simplify the process by letting users make requests in conversational language: You might type "need a flight to O'Hare next weekend" instead of punching info into little boxes.

Acclaimed physicist, author and educator Stephen Hawking has claimed that artificial intelligence may cause the end of civilization as we know it. While I don't think we're headed for a dire, Terminator-like state, there's little doubt that artificial intelligence has the capacity to change the world. That change first affected industrial and mechanical industries by assisting in mass production, but today businesses of all types are discovering the benefits of artificial intelligence in the workplace. Leading organizations are employing artificial intelligence to work alongside employees for more effective and efficient results. Artificial intelligence capabilities can be grouped into three main categories: cognitive computing, machine learning and deep learning.

You probably didn't consider basing your Kentucky Derby bets on artificial intelligence -- but maybe you should have. The artificial intelligence company Unanimous tested its new software platform, UNU, on last weekend's Kentucky Derby, as reported by TechRepublic. Twenty participants, convened by the company, first used the software to narrow the field of 20 horses down to four top picks. The participants then used UNU to predict the winning order -- and it turned out to be 100 percent correct. "I placed my 1 bet on the race at the Derby on Saturday and made 542.10 -- the odds of winning the superfecta [the top 4 finishers in order] were 540-1," TechRepublic reporter Hope Reese wrote.